A Novel Algorithm for Autonomous Transit Network Design
The authors propose a novel algorithm combining neural networks and evolutionary algorithms to optimize autonomous transit network design, outperforming traditional methods.
Redesigning Large-Scale Multimodal Transit Networks with Shared Autonomous Mobility Services
Redesigning multimodal transit networks with Shared Autonomous Mobility Services optimizes costs and enhances service.
Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
The authors use deep reinforcement learning with graph neural networks to learn low-level heuristics for an evolutionary algorithm, which improves the algorithm's results on benchmark synthetic cities and obtains state-of-the-art results when optimizing operating costs. The learned heuristics also improve upon a simulation of the real transit network in the city of Laval, Canada, by as much as 54% and 18% on two key metrics, and offer cost savings of up to 12% over the city's existing transit network.
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